本文整理汇总了Python中control.statesp.StateSpace类的典型用法代码示例。如果您正苦于以下问题：Python StateSpace类的具体用法？Python StateSpace怎么用？Python StateSpace使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了StateSpace类的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_evalfr
def test_evalfr(self):
"""Evaluate the frequency response at one frequency."""
A = [[-2, 0.5], [0.5, -0.3]]
B = [[0.3, -1.3], [0.1, 0.]]
C = [[0., 0.1], [-0.3, -0.2]]
D = [[0., -0.8], [-0.3, 0.]]
sys = StateSpace(A, B, C, D)
resp = [[4.37636761487965e-05 - 0.0152297592997812j,
-0.792603938730853 + 0.0261706783369803j],
[-0.331544857768052 + 0.0576105032822757j,
0.128919037199125 - 0.143824945295405j]]
# Correct versions of the call
np.testing.assert_almost_equal(evalfr(sys, 1j), resp)
np.testing.assert_almost_equal(sys._evalfr(1.), resp)
# Deprecated version of the call (should generate warning)
import warnings
with warnings.catch_warnings(record=True) as w:
# Set up warnings filter to only show warnings in control module
warnings.filterwarnings("ignore")
warnings.filterwarnings("always", module="control")
# Make sure that we get a pending deprecation warning
sys.evalfr(1.)
assert len(w) == 1
assert issubclass(w[-1].category, PendingDeprecationWarning)示例2: test_minrealStaticGain
def test_minrealStaticGain(self):
"""Regression: minreal on static gain was failing"""
g1 = StateSpace([],[],[],[1])
g2 = g1.minreal()
np.testing.assert_array_equal(g1.A, g2.A)
np.testing.assert_array_equal(g1.B, g2.B)
np.testing.assert_array_equal(g1.C, g2.C)
np.testing.assert_array_equal(g1.D, g2.D)示例3: testZero
def testZero(self):
"""Evaluate the zeros of a SISO system."""
sys = StateSpace(self.sys1.A, [[3.], [-2.], [4.]], [[-1., 3., 2.]], [[-4.]])
z = sys.zero()
np.testing.assert_array_almost_equal(z, [4.26864638637134,
-3.75932319318567 + 1.10087776649554j,
-3.75932319318567 - 1.10087776649554j])示例4: testMIMO
def testMIMO(self):
sys = StateSpace([[-0.5, 0.0], [0.0, -1.0]],
[[1.0, 0.0], [0.0, 1.0]],
[[1.0, 0.0], [0.0, 1.0]],
[[0.0, 0.0], [0.0, 0.0]])
omega = np.logspace(-1, 2, 10)
f1 = FRD(sys, omega)
np.testing.assert_array_almost_equal(
sys.freqresp([0.1, 1.0, 10])[0],
f1.freqresp([0.1, 1.0, 10])[0])
np.testing.assert_array_almost_equal(
sys.freqresp([0.1, 1.0, 10])[1],
f1.freqresp([0.1, 1.0, 10])[1])示例5: test_siso
def test_siso(self):
B = np.matrix('0;1')
D = 0
sys = StateSpace(self.A,B,self.C,D)
# test frequency response
frq=sys.freqresp(self.omega)
# test bode plot
bode(sys)
# Convert to transfer function and test bode
systf = tf(sys)
bode(systf)示例6: testMIMOfb2
def testMIMOfb2(self):
sys = StateSpace(np.matrix('-2.0 0 0; 0 -1 1; 0 0 -3'),
np.matrix('1.0 0; 0 0; 0 1'),
np.eye(3), np.zeros((3,2)))
omega = np.logspace(-1, 2, 10)
K = np.matrix('1 0.3 0; 0.1 0 0')
f1 = FRD(sys, omega).feedback(K)
f2 = FRD(sys.feedback(K), omega)
np.testing.assert_array_almost_equal(
f1.freqresp([0.1, 1.0, 10])[0],
f2.freqresp([0.1, 1.0, 10])[0])
np.testing.assert_array_almost_equal(
f1.freqresp([0.1, 1.0, 10])[1],
f2.freqresp([0.1, 1.0, 10])[1])示例7: test_scalarStaticGain
def test_scalarStaticGain(self):
"""Regression: can we create a scalar static gain?"""
g1=StateSpace([],[],[],[2])
g2=StateSpace([],[],[],[3])
# make sure StateSpace internals, specifically ABC matrix
# sizes, are OK for LTI operations
g3 = g1*g2
self.assertEqual(6, g3.D[0,0])
g4 = g1+g2
self.assertEqual(5, g4.D[0,0])
g5 = g1.feedback(g2)
self.assertAlmostEqual(2./7, g5.D[0,0])
g6 = g1.append(g2)
np.testing.assert_array_equal(np.diag([2,3]),g6.D)示例8: testEvalFr
def testEvalFr(self):
"""Evaluate the frequency response at one frequency."""
A = [[-2, 0.5], [0.5, -0.3]]
B = [[0.3, -1.3], [0.1, 0.]]
C = [[0., 0.1], [-0.3, -0.2]]
D = [[0., -0.8], [-0.3, 0.]]
sys = StateSpace(A, B, C, D)
resp = [[4.37636761487965e-05 - 0.0152297592997812j,
-0.792603938730853 + 0.0261706783369803j],
[-0.331544857768052 + 0.0576105032822757j,
0.128919037199125 - 0.143824945295405j]]
np.testing.assert_almost_equal(sys.evalfr(1.), resp)示例9: test_lft
def test_lft(self):
""" test lft function with result obtained from matlab implementation"""
# test case
A = [[1, 2, 3],
[1, 4, 5],
[2, 3, 4]]
B = [[0, 2],
[5, 6],
[5, 2]]
C = [[1, 4, 5],
[2, 3, 0]]
D = [[0, 0],
[3, 0]]
P = StateSpace(A, B, C, D)
Ak = [[0, 2, 3],
[2, 3, 5],
[2, 1, 9]]
Bk = [[1, 1],
[2, 3],
[9, 4]]
Ck = [[1, 4, 5],
[2, 3, 6]]
Dk = [[0, 2],
[0, 0]]
K = StateSpace(Ak, Bk, Ck, Dk)
# case 1
pk = P.lft(K, 2, 1)
Amatlab = [1, 2, 3, 4, 6, 12, 1, 4, 5, 17, 38, 61, 2, 3, 4, 9, 26, 37, 2, 3, 0, 3, 14, 18, 4, 6, 0, 8, 27, 35, 18, 27, 0, 29, 109, 144]
Bmatlab = [0, 10, 10, 7, 15, 58]
Cmatlab = [1, 4, 5, 0, 0, 0]
Dmatlab = [0]
np.testing.assert_allclose(np.array(pk.A).reshape(-1), Amatlab)
np.testing.assert_allclose(np.array(pk.B).reshape(-1), Bmatlab)
np.testing.assert_allclose(np.array(pk.C).reshape(-1), Cmatlab)
np.testing.assert_allclose(np.array(pk.D).reshape(-1), Dmatlab)
# case 2
pk = P.lft(K)
Amatlab = [1, 2, 3, 4, 6, 12, -3, -2, 5, 11, 14, 31, -2, -3, 4, 3, 2, 7, 0.6, 3.4, 5, -0.6, -0.4, 0, 0.8, 6.2, 10, 0.2, -4.2, -4, 7.4, 33.6, 45, -0.4, -8.6, -3]
Bmatlab = []
Cmatlab = []
Dmatlab = []
np.testing.assert_allclose(np.array(pk.A).reshape(-1), Amatlab)
np.testing.assert_allclose(np.array(pk.B).reshape(-1), Bmatlab)
np.testing.assert_allclose(np.array(pk.C).reshape(-1), Cmatlab)
np.testing.assert_allclose(np.array(pk.D).reshape(-1), Dmatlab)示例10: setUp
def setUp(self):
"""Set up a MIMO system to test operations on."""
# sys1: 3-states square system (2 inputs x 2 outputs)
A322 = [[-3., 4., 2.],
[-1., -3., 0.],
[2., 5., 3.]]
B322 = [[1., 4.],
[-3., -3.],
[-2., 1.]]
C322 = [[4., 2., -3.],
[1., 4., 3.]]
D322 = [[-2., 4.],
[0., 1.]]
self.sys322 = StateSpace(A322, B322, C322, D322)
# sys1: 2-states square system (2 inputs x 2 outputs)
A222 = [[4., 1.],
[2., -3]]
B222 = [[5., 2.],
[-3., -3.]]
C222 = [[2., -4],
[0., 1.]]
D222 = [[3., 2.],
[1., -1.]]
self.sys222 = StateSpace(A222, B222, C222, D222)
# sys3: 6 states non square system (2 inputs x 3 outputs)
A623 = np.array([[1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 0, 3, 0, 0, 0],
[0, 0, 0, -4, 0, 0],
[0, 0, 0, 0, -1, 0],
[0, 0, 0, 0, 0, 3]])
B623 = np.array([[0, -1],
[-1, 0],
[1, -1],
[0, 0],
[0, 1],
[-1, -1]])
C623 = np.array([[1, 0, 0, 1, 0, 0],
[0, 1, 0, 1, 0, 1],
[0, 0, 1, 0, 0, 1]])
D623 = np.zeros((3, 2))
self.sys623 = StateSpace(A623, B623, C623, D623)示例11: test_dcgain_integrator
def test_dcgain_integrator(self):
"""DC gain when eigenvalue at DC returns appropriately sized array of nan"""
# the SISO case is also tested in test_dc_gain_{cont,discr}
import itertools
# iterate over input and output sizes, and continuous (dt=None) and discrete (dt=True) time
for inputs,outputs,dt in itertools.product(range(1,6),range(1,6),[None,True]):
states = max(inputs,outputs)
# a matrix that is singular at DC, and has no "useless" states as in _remove_useless_states
a = np.triu(np.tile(2,(states,states)))
# eigenvalues all +2, except for ...
a[0,0] = 0 if dt is None else 1
b = np.eye(max(inputs,states))[:states,:inputs]
c = np.eye(max(outputs,states))[:outputs,:states]
d = np.zeros((outputs,inputs))
sys = StateSpace(a,b,c,d,dt)
dc = np.squeeze(np.tile(np.nan,(outputs,inputs)))
np.testing.assert_array_equal(dc, sys.dcgain())示例12: test_copy_constructor
def test_copy_constructor(self):
# Create a set of matrices for a simple linear system
A = np.array([[-1]])
B = np.array([[1]])
C = np.array([[1]])
D = np.array([[0]])
# Create the first linear system and a copy
linsys = StateSpace(A, B, C, D)
cpysys = StateSpace(linsys)
# Change the original A matrix
A[0, 0] = -2
np.testing.assert_array_equal(linsys.A, [[-1]]) # original value
np.testing.assert_array_equal(cpysys.A, [[-1]]) # original value
# Change the A matrix for the original system
linsys.A[0, 0] = -3
np.testing.assert_array_equal(cpysys.A, [[-1]]) # original value示例13: testMinreal
def testMinreal(self):
"""Test a minreal model reduction"""
#A = [-2, 0.5, 0; 0.5, -0.3, 0; 0, 0, -0.1]
A = [[-2, 0.5, 0], [0.5, -0.3, 0], [0, 0, -0.1]]
#B = [0.3, -1.3; 0.1, 0; 1, 0]
B = [[0.3, -1.3], [0.1, 0.], [1.0, 0.0]]
#C = [0, 0.1, 0; -0.3, -0.2, 0]
C = [[0., 0.1, 0.0], [-0.3, -0.2, 0.0]]
#D = [0 -0.8; -0.3 0]
D = [[0., -0.8], [-0.3, 0.]]
# sys = ss(A, B, C, D)
sys = StateSpace(A, B, C, D)
sysr = sys.minreal()
self.assertEqual(sysr.states, 2)
self.assertEqual(sysr.inputs, sys.inputs)
self.assertEqual(sysr.outputs, sys.outputs)
np.testing.assert_array_almost_equal(
eigvals(sysr.A), [-2.136154, -0.1638459])示例14: test_matrixStaticGain
def test_matrixStaticGain(self):
"""Regression: can we create matrix static gains?"""
d1 = np.matrix([[1,2,3],[4,5,6]])
d2 = np.matrix([[7,8],[9,10],[11,12]])
g1=StateSpace([],[],[],d1)
# _remove_useless_states was making A = [[0]]
self.assertEqual((0,0), g1.A.shape)
g2=StateSpace([],[],[],d2)
g3=StateSpace([],[],[],d2.T)
h1 = g1*g2
np.testing.assert_array_equal(d1*d2, h1.D)
h2 = g1+g3
np.testing.assert_array_equal(d1+d2.T, h2.D)
h3 = g1.feedback(g2)
np.testing.assert_array_almost_equal(solve(np.eye(2)+d1*d2,d1), h3.D)
h4 = g1.append(g2)
np.testing.assert_array_equal(block_diag(d1,d2),h4.D)示例15: testAppendTF
def testAppendTF(self):
"""Test appending a state-space system with a tf"""
A1 = [[-2, 0.5, 0], [0.5, -0.3, 0], [0, 0, -0.1]]
B1 = [[0.3, -1.3], [0.1, 0.], [1.0, 0.0]]
C1 = [[0., 0.1, 0.0], [-0.3, -0.2, 0.0]]
D1 = [[0., -0.8], [-0.3, 0.]]
s = TransferFunction([1, 0], [1])
h = 1/(s+1)/(s+2)
sys1 = StateSpace(A1, B1, C1, D1)
sys2 = _convertToStateSpace(h)
sys3c = sys1.append(sys2)
np.testing.assert_array_almost_equal(sys1.A, sys3c.A[:3,:3])
np.testing.assert_array_almost_equal(sys1.B, sys3c.B[:3,:2])
np.testing.assert_array_almost_equal(sys1.C, sys3c.C[:2,:3])
np.testing.assert_array_almost_equal(sys1.D, sys3c.D[:2,:2])
np.testing.assert_array_almost_equal(sys2.A, sys3c.A[3:,3:])
np.testing.assert_array_almost_equal(sys2.B, sys3c.B[3:,2:])
np.testing.assert_array_almost_equal(sys2.C, sys3c.C[2:,3:])
np.testing.assert_array_almost_equal(sys2.D, sys3c.D[2:,2:])
np.testing.assert_array_almost_equal(sys3c.A[:3,3:], np.zeros( (3, 2)) )
np.testing.assert_array_almost_equal(sys3c.A[3:,:3], np.zeros( (2, 3)) )